Scholarlink

Background <p>Perioperative neurocognitive disorder (PND) is a serious complication in older adults following hip fracture surgery, associated with poor functional recovery and increased mortality. Existing prediction models often focus solely on in-hospital delirium, neglecting early post-discharge cognitive decline. This study aimed to develop and externally validate a dynamic nomogram to predict early PND, defined as cognitive impairment occurring within 3&#xa0;months postoperatively.</p> Methods <p>This multicenter, retrospective cohort study included patients aged ≥ 65&#xa0;years undergoing hip fracture surgery at two medical centers. Patients from the primary center were temporally allocated to a training cohort (<i>n</i> = 640) and an internal validation cohort (<i>n</i> = 160). An independent external validation cohort (<i>n</i> = 137) was collected from a second institution. The primary outcome was early PND, a composite of in-hospital delirium and cognitive decline assessed up to 3&#xa0;months post-discharge. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for predictor selection. A multivariable logistic regression model was developed and visualized as a nomogram. Model performance was assessed via the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).</p> Results <p>Early PND occurred in 17.7% of the training cohort. Eight independent predictors were identified: age, body mass index, diabetes, frailty, ASA classification, albumin-to-fibrinogen ratio, neutrophil-to-lymphocyte ratio, and duration of surgery. The nomogram demonstrated good discrimination in the training cohort (AUC, 0.875; 95% CI, 0.841–0.910) and internal validation cohort (AUC, 0.869; 95% CI, 0.808–0.930). In the external validation cohort, the model maintained satisfactory discrimination (AUC, 0.775; 95% CI, 0.675–0.875). Calibration plots showed reasonable agreement between predicted and observed probabilities, and DCA indicated positive net clinical benefit across clinically relevant threshold probabilities in all cohorts.</p> Conclusions <p>We developed and externally validated a dynamic nomogram that accurately predicts early PND in older adults after hip fracture surgery. By incorporating readily available clinical and laboratory variables, this tool facilitates early risk stratification and may guide targeted perioperative interventions to improve cognitive outcomes.</p> Trial registration <p>Chinese Clinical Trial Registry, ChiCTR2500107395. Registered August 11, 2025.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and multicenter validation of a dynamic nomogram for early postoperative neurocognitive disorder in older adults with hip fracture

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月11日

查看全文
  • Shirong Wei,
  • Xin Xiang,
  • Sitong Zhou,
  • Junwen Tu,
  • Tong Zhi,
  • Zhangtian Xia,
  • Qihong Shen,
  • Chaobo Ni,
  • Tesheng Gao,
  • Ming Yao,
  • Huadong Ni

摘要

Background

Perioperative neurocognitive disorder (PND) is a serious complication in older adults following hip fracture surgery, associated with poor functional recovery and increased mortality. Existing prediction models often focus solely on in-hospital delirium, neglecting early post-discharge cognitive decline. This study aimed to develop and externally validate a dynamic nomogram to predict early PND, defined as cognitive impairment occurring within 3 months postoperatively.

Methods

This multicenter, retrospective cohort study included patients aged ≥ 65 years undergoing hip fracture surgery at two medical centers. Patients from the primary center were temporally allocated to a training cohort (n = 640) and an internal validation cohort (n = 160). An independent external validation cohort (n = 137) was collected from a second institution. The primary outcome was early PND, a composite of in-hospital delirium and cognitive decline assessed up to 3 months post-discharge. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for predictor selection. A multivariable logistic regression model was developed and visualized as a nomogram. Model performance was assessed via the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

Results

Early PND occurred in 17.7% of the training cohort. Eight independent predictors were identified: age, body mass index, diabetes, frailty, ASA classification, albumin-to-fibrinogen ratio, neutrophil-to-lymphocyte ratio, and duration of surgery. The nomogram demonstrated good discrimination in the training cohort (AUC, 0.875; 95% CI, 0.841–0.910) and internal validation cohort (AUC, 0.869; 95% CI, 0.808–0.930). In the external validation cohort, the model maintained satisfactory discrimination (AUC, 0.775; 95% CI, 0.675–0.875). Calibration plots showed reasonable agreement between predicted and observed probabilities, and DCA indicated positive net clinical benefit across clinically relevant threshold probabilities in all cohorts.

Conclusions

We developed and externally validated a dynamic nomogram that accurately predicts early PND in older adults after hip fracture surgery. By incorporating readily available clinical and laboratory variables, this tool facilitates early risk stratification and may guide targeted perioperative interventions to improve cognitive outcomes.

Trial registration

Chinese Clinical Trial Registry, ChiCTR2500107395. Registered August 11, 2025.